Triple

T12969769
Position Surface form Disambiguated ID Type / Status
Subject Pirkanmaa E321361 entity
Predicate hasCity P316 FINISHED
Object Parkano E548630 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Parkano | Statement: [Pirkanmaa, hasCity, Parkano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Parkano
Context triple: [Pirkanmaa, hasCity, Parkano]
  • A. Parkano chosen
    Parkano is a small town and municipality in the Pirkanmaa region of western Finland, known for its forests, lakes, and position along key transport routes.
  • B. Pojuca
    Pojuca is a municipality in the Brazilian state of Bahia that forms part of the greater Salvador metropolitan area.
  • C. Opata
    Opata refers to an Indigenous people and their now largely extinct Uto-Aztecan language historically spoken in northern Mexico, particularly in the present-day state of Sonora.
  • D. Bibinje
    Bibinje is a coastal village and popular tourist destination on the Adriatic Sea in Croatia, located just southeast of the city of Zadar.
  • E. Nogliki
    Nogliki is a small town and administrative center in northern Sakhalin, Russia, known primarily for its role in supporting the island’s oil and gas industry.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e407e5081909424fc0c22483c28 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8e6b31c8190b09276003f284f25 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 8:34 p.m.